Ebook: mODa 10 – Advances in Model-Oriented Design and Analysis: Proceedings of the 10th International Workshop in Model-Oriented Design and Analysis Held in Łagów Lubuski, Poland, June 10–14, 2013

This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.

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This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.

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This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.
Content:
Front Matter....Pages I-XX
A Convergent Algorithm for Finding KL-Optimum Designs and Related Properties....Pages 1-9
Robust Experimental Design for Choosing Between Models of Enzyme Inhibition....Pages 11-18
Checking Linear Regression Models Taking Time into Account....Pages 19-26
Optimal Sample Proportion for a Two-Treatment Clinical Trial in the Presence of Surrogate Endpoints....Pages 27-34
Estimating and Quantifying Uncertainties on Level Sets Using the Vorob’ev Expectation and Deviation with Gaussian Process Models....Pages 35-43
Optimal Designs for Multiple-Mixture by Process Variable Experiments....Pages 45-53
Optimal Design of Experiments for Delayed Responses in Clinical Trials....Pages 55-61
Construction of Minimax Designs for the Trinomial Spike Model in Contingent Valuation Experiments....Pages 63-72
Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling....Pages 73-80
Randomization Based Inference for the Drop-The-Loser Rule....Pages 81-89
Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response....Pages 91-98
Randomly Reinforced Urn Designs Whose Allocation Proportions Converge to Arbitrary Prespecified Values....Pages 99-106
Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity....Pages 107-115
Optimal Design for Count Data with Binary Predictors in Item Response Theory....Pages 117-124
Differences between Analytic and Algorithmic Choice Designs for Pairs of Partial Profiles....Pages 125-133
Approximate Bayesian Computation Design (ABCD), an Introduction....Pages 135-143
Approximation of the Fisher Information Matrix for Nonlinear Mixed Effects Models in Population PK/PD Studies....Pages 145-152
c-Optimal Designs for the Bivariate Emax Model....Pages 153-161
On the Functional Approach to Locally D-Optimum Design for Multiresponse Models....Pages 163-170
Sample Size Calculation for Diagnostic Tests in Generalized Linear Mixed Models....Pages 171-178
D-Optimal Designs for Lifetime Experiments with Exponential Distribution and Censoring....Pages 179-186
Convergence of an Algorithm for Constructing Minimax Designs....Pages 187-194
Extended Optimality Criteria for Optimum Design in Nonlinear Regression....Pages 195-202
Optimal Design for Multivariate Models with Correlated Observations....Pages 203-210
Optimal Designs for the Prediction of Individual Effects in Random Coefficient Regression....Pages 211-218
D-Optimum Input Signals for Systems with Spatio-Temporal Dynamics....Pages 219-227
Random Projections in Model Selection and Related Experimental Design Problems....Pages 229-236
Optimal Design for the Bounded Log-Linear Regression Model....Pages 237-245
Back Matter....Pages 247-249

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This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.
Content:
Front Matter....Pages I-XX
A Convergent Algorithm for Finding KL-Optimum Designs and Related Properties....Pages 1-9
Robust Experimental Design for Choosing Between Models of Enzyme Inhibition....Pages 11-18
Checking Linear Regression Models Taking Time into Account....Pages 19-26
Optimal Sample Proportion for a Two-Treatment Clinical Trial in the Presence of Surrogate Endpoints....Pages 27-34
Estimating and Quantifying Uncertainties on Level Sets Using the Vorob’ev Expectation and Deviation with Gaussian Process Models....Pages 35-43
Optimal Designs for Multiple-Mixture by Process Variable Experiments....Pages 45-53
Optimal Design of Experiments for Delayed Responses in Clinical Trials....Pages 55-61
Construction of Minimax Designs for the Trinomial Spike Model in Contingent Valuation Experiments....Pages 63-72
Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling....Pages 73-80
Randomization Based Inference for the Drop-The-Loser Rule....Pages 81-89
Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response....Pages 91-98
Randomly Reinforced Urn Designs Whose Allocation Proportions Converge to Arbitrary Prespecified Values....Pages 99-106
Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity....Pages 107-115
Optimal Design for Count Data with Binary Predictors in Item Response Theory....Pages 117-124
Differences between Analytic and Algorithmic Choice Designs for Pairs of Partial Profiles....Pages 125-133
Approximate Bayesian Computation Design (ABCD), an Introduction....Pages 135-143
Approximation of the Fisher Information Matrix for Nonlinear Mixed Effects Models in Population PK/PD Studies....Pages 145-152
c-Optimal Designs for the Bivariate Emax Model....Pages 153-161
On the Functional Approach to Locally D-Optimum Design for Multiresponse Models....Pages 163-170
Sample Size Calculation for Diagnostic Tests in Generalized Linear Mixed Models....Pages 171-178
D-Optimal Designs for Lifetime Experiments with Exponential Distribution and Censoring....Pages 179-186
Convergence of an Algorithm for Constructing Minimax Designs....Pages 187-194
Extended Optimality Criteria for Optimum Design in Nonlinear Regression....Pages 195-202
Optimal Design for Multivariate Models with Correlated Observations....Pages 203-210
Optimal Designs for the Prediction of Individual Effects in Random Coefficient Regression....Pages 211-218
D-Optimum Input Signals for Systems with Spatio-Temporal Dynamics....Pages 219-227
Random Projections in Model Selection and Related Experimental Design Problems....Pages 229-236
Optimal Design for the Bounded Log-Linear Regression Model....Pages 237-245
Back Matter....Pages 247-249
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